3 papers
cs.LG2025
Upside Down Reinforcement Learning with Policy Generators
Jacopo Di Ventura, Dylan R. Ashley, Vincent Herrmann +2
Upside Down Reinforcement Learning (UDRL) is a promising framework for solving reinforcement learning problems which focuses on learning command-conditioned policies. In this work,…
cs.CV2024
Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable
Haozhe Liu, Wentian Zhang, Bing Li +2
Foundational generative models should be traceable to protect their owners and facilitate safety regulation. To achieve this, traditional approaches embed identifiers based on supe…
cs.LG2022
Learning Relative Return Policies With Upside-Down Reinforcement Learning
Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber +1
Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems. Recent work in this area has largely focused on learning comm…